CN105306843A - Dead pixel processing method and system for image sensor - Google Patents
Dead pixel processing method and system for image sensor Download PDFInfo
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Abstract
Embodiments of the invention disclose a dead pixel processing method and system for an image sensor. The method comprises the following steps: presetting a mapping table of bad cluster types and interpolation algorithms; detecting and storing position information of a dead pixel and bad cluster type information corresponding to the dead pixel; and looking up the dead pixel in a collected image according to the position information of the dead pixel, selecting a matched interpolation algorithm in the mapping table according to the bad cluster type information corresponding to the dead pixel, and using the interpolation algorithm to carry out interpolation processing on the dead pixel. According to the dead pixel processing method and system for image sensor provided by the embodiments of the invention, bad clusters are classified according to a distribution type of peripheral dead pixels, different interpolation algorithms are adopted for different bad cluster types corresponding to the dead pixels, thus each dead pixel can obtain an optimal interpolation result, and then the dead pixel processing effect of the image sensor is improved.
Description
Technical field
The present invention relates to image sensor technologies field, particularly relate to a kind of bad point processing method and system of imageing sensor.
Background technology
Imageing sensor is a kind of device optical imagery being converted to electronic signal, is widely used in the fields such as shooting, IMAQ and commercial measurement.Usually, an imageing sensor comprises a large amount of photosensitive units, and each photosensitive unit corresponds to a pixel in imageing sensor institute output image.
Due to aspects such as manufacturing process, transport or storing modes, some photosensitive unit of imageing sensor is damaged and can not be normally photosensitive, the corresponding in the picture pixel of the photosensitive unit that these can not be normally photosensitive be called as bad point.Wherein, bad point mainly comprises following three types: one, dim spot bad point, and such bad point is formed by thoroughly damaging mainly due to photosensitive unit, and performance is a dim spot in the picture; Two, bright spot bad point, such bad point is formed mainly due to the photosensitive diode in photosensitive unit and the direct short circuit of power supply, makes the voltage of photosensitive unit be in high level always, and performance is a bright spot in the picture; Three, unstable bad point, such bad point is mainly due to the deviation of manufacture view, the response of photosensitive unit to light intensity is made to differ from the normal response of photosensitive unit around it, its pixel value is more higher than the pixel value of surrounding pixel point or on the low side in the picture in performance, and this type of bad point can change with the change of external environment the impact of image.Known accordingly, the image quality tool of bad point to image has a certain impact, and therefore, must process bad point.
In prior art, the processing mode of bad point mainly comprises the following steps:
1) bad point detection: detect the positional information of bad point in advance and preserve;
2) bad point corrects: the positional information according to bad point searches bad point in the image gathered, and utilizes the interpolation algorithm preset to carry out interpolation processing to bad point.
Wherein, interpolation processing is carried out to bad point, is specially: utilize the pixel value of interpolation algorithm to bad point surrounding pixel point to carry out computing, the pixel value of operation result as bad point is replaced its original pixel values.Interpolation algorithm conventional in prior art mainly contains two kinds: a kind of is be weighted on average, using weighted average as the pixel value of bad point to the pixel value of bad point surrounding pixel point; Another kind is the variation tendency considering image information, adopts the pixel value of gradient algorithm to bad point surrounding pixel point to carry out computing.But, the position occurred due to quantity and the bad point of bad point in the image that imageing sensor gathers has randomness, cause the distribution pattern of bad point varied, existing interpolation algorithm is adopted not to have good treatment effect to all bad points, especially when bad point distribution is comparatively concentrated, the bad point of carrying out interpolation likely can influence each other, and then the imaging effect of effect diagram image-position sensor.
Summary of the invention
A kind of bad point processing method and system of imageing sensor is provided in the embodiment of the present invention, poor to solve the treatment effect of bad point in prior art, and then the technical problem causing the imaging effect of imageing sensor poor.
In order to solve the problems of the technologies described above, the embodiment of the invention discloses following technical scheme:
A bad point processing method for imageing sensor, described method comprises:
Preset the mapping table of bad bunch type and interpolation algorithm; Detect the positional information of bad point and the evil idea bunch type information corresponding to bad point, and store; Positional information according to bad point searches bad point in the image gathered, and the evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table, utilizes this interpolation algorithm to carry out interpolation processing to described bad point; Wherein, described evil idea bunch type is specially: the distribution pattern of peripheral bad point in the module inner peripheral pixel in image centered by bad point.
Preferably, described module centered by bad point is 3 × 3 modules, the definition left and right of bad point, lower-left upper right, up and down and upper left lower right be four distribution arrangements of peripheral pixel in 3 × 3 modules, occupied by peripheral bad point, the quantity of distribution arrangement and/or the quantity of peripheral bad point conclude the distribution pattern of peripheral bad point.
Preferably, the distribution pattern of the peripheral bad point of described conclusion, comprising: if there is not peripheral bad point or only there is a peripheral bad point, be then summarized as the first distribution pattern; If peripheral bad point only occupies a distribution arrangement, and this distribution arrangement has two peripheral bad points, be then summarized as the second distribution pattern; If peripheral bad point occupies two distribution arrangements, be then summarized as the 3rd distribution pattern; If peripheral bad point occupies three distribution arrangements, be then summarized as the 4th distribution pattern.
Preferably, first distribution pattern of described peripheral bad point, the second distribution pattern, the 3rd distribution pattern, the 4th distribution pattern bad bunch of the first kind, bad bunch of Equations of The Second Kind, bad bunch of the 3rd class, bad bunch of the 4th class respectively in corresponding bad bunch type, the mapping table of described evil idea bunch type and described interpolation algorithm, is specially:
If described evil idea bunch is bad bunch of the first kind, described interpolation algorithm corresponds to: on four distribution arrangements, adopt gradient algorithm; If described evil idea bunch is bad bunch of Equations of The Second Kind, described interpolation algorithm corresponds to: on three distribution arrangements of other except distribution arrangement occupied by peripheral bad point, adopt gradient algorithm; If described evil idea bunch is the 3rd class bad bunch, described interpolation algorithm corresponds to: on two distribution arrangements of other except distribution arrangement occupied by peripheral bad point, adopt bilinear interpolation; If described evil idea bunch is the 4th class bad bunch, described interpolation algorithm corresponds to: on the distribution arrangement that there is not peripheral bad point, adopt the two-value method of average.
Preferably, before the described bunch type information of evil idea corresponding to bad point selects the interpolation algorithm matched in the mapping table, also comprise: judge whether bad point is marginal point, if so, then adopt indirect assignment method to carry out interpolation processing to described marginal point; Wherein, described indirect assignment method is specially: if described marginal point is corner point, then adopt the pixel value of the horizontal adjacent pixels of described marginal point point to substitute the pixel value of described marginal point; Otherwise, adopt two of described marginal point level average pixel values that are adjacent or two vertical neighbor pixels to substitute the pixel value of described marginal point.
A bad point processing method for imageing sensor, for color image sensor, described method comprises: the mapping table presetting bad bunch type and interpolation algorithm; According to the color of pixel in the coloured image gathered, coloured image is resolved into the submodule of different colours; Detect the positional information of bad point in each submodule and the evil idea bunch type information corresponding to bad point successively, and store; The pixel value of different colours in coloured image is normalized, the pixel corresponding to bad point is searched according to the positional information of bad point, evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table, utilizes this interpolation algorithm to carry out interpolation processing to the pixel corresponding to described bad point; Wherein, described evil idea bunch type is specially: the distribution pattern of peripheral bad point in the module inner peripheral pixel in coloured image centered by bad point.
A bad point treatment system for imageing sensor, described system comprises:
Imageing sensor, for gathering image; Detection module, for detecting the evil idea bunch type information corresponding to the positional information of bad point and bad point; Memory module, for receiving and storing the evil idea bunch type information corresponding to the positional information of the bad point that detection module detects and bad point, and the mapping table of prestore bad bunch type and interpolation algorithm; Image processing module, for receiving the image of collection, and image procossing is carried out to the image gathered, be specially: extract the positional information of bad point and the evil idea bunch type information corresponding to bad point in a storage module, positional information according to bad point searches bad point in the image gathered, evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table of memory module, utilizes this interpolation algorithm to carry out interpolation processing to described bad point; Wherein, described evil idea bunch type is specially: the distribution pattern of peripheral bad point in the module inner peripheral pixel in image centered by bad point.
Preferably, described module centered by bad point is 3 × 3 modules, the definition left and right of bad point, lower-left upper right, up and down and upper left lower right be four distribution arrangements of peripheral pixel in 3 × 3 modules, occupied by peripheral bad point, the quantity of distribution arrangement and/or the quantity of peripheral bad point conclude the distribution pattern of peripheral bad point.
Preferably, the distribution pattern of the peripheral bad point of described conclusion, comprising: if there is not peripheral bad point or only there is a peripheral bad point, be then summarized as the first distribution pattern; If peripheral bad point only occupies a distribution arrangement, and this distribution arrangement has two peripheral bad points, be then summarized as the second distribution pattern; If peripheral bad point occupies two distribution arrangements, be then summarized as the 3rd distribution pattern; If peripheral bad point occupies three distribution arrangements, be then summarized as the 4th distribution pattern.
Preferably, first distribution pattern of described peripheral bad point, the second distribution pattern, the 3rd distribution pattern, the 4th distribution pattern bad bunch of the first kind, bad bunch of Equations of The Second Kind, bad bunch of the 3rd class, bad bunch of the 4th class respectively in corresponding bad bunch type, the mapping table of described evil idea bunch type and described interpolation algorithm, be specially: if described evil idea bunch is bad bunch of the first kind, described interpolation algorithm corresponds to: on four distribution arrangements, adopt gradient algorithm; If described evil idea bunch is bad bunch of Equations of The Second Kind, described interpolation algorithm corresponds to: on three distribution arrangements of other except distribution arrangement occupied by peripheral bad point, adopt gradient algorithm; If described evil idea bunch is the 3rd class bad bunch, described interpolation algorithm corresponds to: on two distribution arrangements of other except distribution arrangement occupied by peripheral bad point, adopt bilinear interpolation; If described evil idea bunch is the 4th class bad bunch, described interpolation algorithm corresponds to: on the distribution arrangement that there is not peripheral bad point, adopt the two-value method of average.
Preferably, before the described bunch type information of evil idea corresponding to bad point selects the interpolation algorithm matched in the mapping table, also comprise: judge whether bad point is marginal point, if so, then adopt indirect assignment method to carry out interpolation processing to described marginal point; Wherein, described indirect assignment method is specially: if described marginal point is corner point, then adopt the pixel value of the horizontal adjacent pixels of described marginal point point to substitute the pixel value of described marginal point; Otherwise, adopt two of described marginal point level average pixel values that are adjacent or two vertical neighbor pixels to substitute the pixel value of described marginal point.
A bad point treatment system for imageing sensor, described system comprises: color image sensor, for gathering coloured image; Decomposing module, for resolving into the submodule of different colours by coloured image according to the color of pixel in coloured image; Detection module, for detecting the evil idea bunch type information corresponding to the positional information of bad point in each submodule and bad point successively, and stores the information transmission detected to memory module; Memory module, for receiving and storing the evil idea bunch type information corresponding to the positional information of the bad point that detection module detects and bad point, and the mapping table of prestore bad bunch type and interpolation algorithm; Image processing module, for receiving coloured image, and image procossing is carried out to coloured image, be specially: the pixel value of different colours in coloured image is normalized, extract the positional information of bad point and the evil idea bunch type information corresponding to bad point in a storage module, positional information according to bad point searches bad point in coloured image, evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table of memory module, utilizes this interpolation algorithm to carry out interpolation processing to described bad point; Wherein, described evil idea bunch type is specially: the distribution pattern of peripheral bad point in the module inner peripheral pixel in coloured image centered by bad point.
From above technical scheme, the bad point processing method of a kind of imageing sensor that the embodiment of the present invention provides and system are classified to bad bunch according to the distribution pattern of peripheral bad point, different interpolation algorithms is adopted for the bad bunch type of the difference corresponding to bad point, make each bad point can obtain optimum interpolation result, and then improve the bad point treatment effect of imageing sensor.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, for those of ordinary skills, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
The bad point process flow schematic diagram of a kind of imageing sensor that Fig. 1 provides for the embodiment of the present invention;
Fig. 2 is the distribution arrangement schematic diagram of peripheral pixel in the embodiment of the present invention 3 × 3 module;
Fig. 3 is the distribution pattern of the peripheral bad point of the embodiment of the present invention and a kind of corresponding relation schematic diagram of bad bunch;
Fig. 4 is symmetrical bad bunch schematic diagram in the embodiment of the present invention bad bunch of group is organized with bad bunch;
Fig. 5 is the determination methods schematic flow sheet in step S120;
The bad point process flow schematic diagram of a kind of color image sensor that Fig. 6 provides for the embodiment of the present invention
Fig. 7 is the coloured image schematic diagram of Bayer form;
Fig. 8 is that the coloured image of Bayer form is decomposed into submodule schematic diagram.
Embodiment
Technical scheme in the present invention is understood better in order to make those skilled in the art person, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, should belong to the scope of protection of the invention.
The bad point processing method of a kind of imageing sensor that the embodiment of the present invention provides and system also detect the evil idea bunch type information corresponding with bad point except detecting the positional information of bad point, i.e. the distributed intelligence of other bad point (hereinafter referred to as peripheral bad point) in bad point surrounding pixel point.During owing to carrying out interpolation processing to bad point in bad point correction, peripheral bad point may affect to interpolation result, and the impact that the peripheral bad point of different distributions type produces interpolation algorithms different in interpolation processing is different, therefore, the embodiment of the present invention is classified to bad bunch according to the distribution pattern of peripheral bad point, different interpolation algorithms is adopted for the bad bunch type of the difference corresponding to bad point, the impact of peripheral bad point on interpolation algorithm is dropped to minimum, make each bad point can obtain optimum interpolation result, and then improve the bad point treatment effect of imageing sensor.
The bad point process flow schematic diagram of a kind of imageing sensor that Fig. 1 provides for the embodiment of the present invention, this method is mainly used in black and white image transducer, namely carry out bad point process to the black and white image that black and white image transducer gathers, as shown in Figure 1, it comprises the steps:
Step S100: the mapping table presetting bad bunch type and interpolation algorithm;
The distribution pattern of peripheral bad point in module inner peripheral pixel in the image that bad bunch type and imageing sensor gather, centered by bad point.Due to different interpolation algorithms will be adopted for different evil ideas bunch type in embodiments of the present invention, therefore, need the exhaustive evil idea bunch type that may occur in advance, and the corresponding relation of bad bunch type and interpolation algorithm is set, i.e. mapping table.Understand the present invention for the ease of those skilled in the art, the mapping table of distribution pattern to bad bunch type and interpolation algorithm below by way of bad point peripheral in 3 × 3 modules is illustrated.
In 3 × 3 modules, comprise pixel and 8 peripheral pixels points that 1 is positioned at center altogether, owing to being carry out interpolation processing to the pixel being positioned at center, the pixel being therefore positioned at center must be bad point.In addition, if the quantity of peripheral bad point is more than 6, the bad point being positioned at center is too much caused to repair by due to the quantity of bad point in bad bunch; If the quantity of peripheral bad point is greater than 3 and be less than 6, then part bad point can be repaired, and in this situation, a bad bunch type comprises and more than foregoing Four types, in like manner can increase bad bunch type successively according to peripheral bad point distribution; If the quantity of peripheral bad point is less than or equal to 3, then all bad points all can be repaired, bad bunch type and foregoing Four types.Therefore, the quantity only enumerating peripheral bad point is below less than or equal to the evil idea bunch type of 3.
Fig. 2 is the distribution arrangement schematic diagram of peripheral pixel in the embodiment of the present invention 3 × 3 module, as shown in Figure 2, in 3 × 3 modules centered by bad point, the left and right (0 °) of definition bad point, lower-left upper right (45 °), (90 °) and bottom right, upper left (135 °) are respectively first of peripheral pixel in 3 × 3 modules up and down, second, 3rd and the 4th distribution arrangement, in embodiments of the present invention, occupied by peripheral bad point, the quantity of distribution arrangement and/or the quantity of peripheral bad point conclude the distribution pattern of peripheral bad point, describe in detail below in conjunction with Fig. 3.
Fig. 3 is the distribution pattern of the peripheral bad point of the embodiment of the present invention and a kind of corresponding relation schematic diagram of bad bunch, in order to save length, evil idea bunch example hurdle in figure 3, the evil idea bunch with symmetric relation is summarized as one group, and its institute of noted in parentheses after bad bunch of each group comprises the quantity of symmetrical evil idea bunch.Such as, in the diagram, be 4 in bad bunch of group bracket below, then there are in corresponding this evil idea bunch group 4 symmetrical evil ideas bunch.In addition; it is to be noted; one or more evil ideas bunches that evil idea bunch example hurdle in Fig. 3 only have chosen in each distribution pattern carry out exemplary illustration, all evil ideas bunch not in exhaustive each distribution pattern, but should not it can be used as limiting the scope of the invention.
In the present embodiment, if there is not peripheral bad point or only there is a peripheral bad point, then the first distribution pattern is summarized as; If peripheral bad point only occupies a distribution arrangement, and this distribution arrangement has two peripheral bad points, be then summarized as the second distribution pattern; If peripheral bad point occupies two distribution arrangements, be then summarized as the 3rd distribution pattern; If peripheral bad point occupies three distribution arrangements, be then summarized as the 4th distribution pattern.
It is to be noted; the distribution pattern of above-mentioned peripheral bad point is only a kind of concrete execution mode of the embodiment of the present invention with the corresponding relation of bad bunch; those skilled in the art can correspondingly according to actual needs adjust, and it should fall within protection scope of the present invention equally.
Wherein, first distribution pattern of peripheral bad point, the second distribution pattern, the 3rd distribution pattern, the 4th distribution pattern bad bunch of the first kind, bad bunch of Equations of The Second Kind, bad bunch of the 3rd class, bad bunch of the 4th class respectively in corresponding bad bunch type, the mapping table of described evil idea bunch type and described interpolation algorithm as shown in Table 1.
Table one:
In all interpolation algorithms, the most frequently used has gradient algorithm, the method for average and indirect assignment method, supposes that bad point is expressed as g (x, y) (wherein, x, y represent bad point coordinate in the picture), below gradient algorithm, the method for average and indirect assignment method are illustrated.
Gradient algorithm:
Calculate the First-order Gradient of each distribution arrangement, and compare the size of First-order Gradient value on each distribution arrangement, because direction that First-order Gradient value is minimum exists the maximum probability of flat site, therefore, bad point correction is carried out in the minimum direction of preferred First-order Gradient value, and namely gradient method mainly comprises the following steps:
1) size of First-order Gradient on four distribution arrangements is compared;
2) get the corrected value of average as bad point of normal pixel point on minimal gradient direction, interpolation processing is carried out to bad point.
In addition, improve the correction accuracy of bad point if think further, can also assign weight for each gradient direction, the corrected value of bad point is exactly the weighted average of its surrounding normal pixel.
Wherein, for bad bunch of the first kind, four distribution arrangements adopt gradient algorithm, its computing formula such as formula shown in one,
Formula one:
For bad bunch of Equations of The Second Kind, because peripheral bad point occupies a distribution arrangement, therefore, three distribution arrangements of other except distribution arrangement occupied by peripheral bad point adopt gradient algorithm, and its computing formula is the fraction that formula one deducts correspondence direction.
Averaging method:
Utilize the pixel value of average to bad point of peripheral pixels point to substitute, its computing formula such as formula shown in two,
Formula two:
Wherein, x ' can get, and { { y-1, y, y+1} (can not get x and y) simultaneously, represent the pixel coordinate around bad point for x-1, x, x+1}, y ' can get.
For bad bunch of the 3rd class, because peripheral bad point occupies two distribution arrangements, therefore, two distribution arrangements of other except distribution arrangement occupied by peripheral bad point adopt averaging method, i.e. bilinear interpolation.
For bad bunch of the 4th class, because peripheral bad point occupies three distribution arrangements, therefore, the distribution arrangement of other except distribution arrangement occupied by peripheral bad point adopts averaging method.
Indirect assignment method:
Directly substitute with the normal pixel point of the most close position (upper and lower, left and right) of bad point, for the bad point of specific position, as marginal point or angle point, preferred indirect assignment method carries out interpolation processing.
Step S110: detect the positional information of bad point and the evil idea bunch type information corresponding to bad point, and store;
In embodiments of the present invention, except will detecting the positional information of bad point, also to detect the position relationship information between bad point, namely with bunch type information of the evil idea corresponding to bad point.Be specially, first detect the positional information of each bad point; Secondly, analyze the distribution pattern of peripheral bad point in the peripheral pixels point of each bad point one by one, and then summarize the evil idea bunch type corresponding to this bad point, and store.
Wherein, the detection of bad point adopts the method being similar to flat field correction, namely by the inconsistency screening bad point of photosensitive unit on sensor chip.Ideally, when camera is to uniform target imaging, the gray value obtaining all pixels in image should be identical in theory, but by the impact of the factor such as ambient lighting, processing technology, in output image, the value of each pixel often has larger difference.
Usually for single pixel, its response gray value and incident intensity are linear, and can write: Y=aX+b, wherein, slope a can regard signal gain as, and intercept b can regard signal side-play amount as, and X represents incident intensity, and Y is the response of output.
As shown in the above, on transducer, the response of different pixels point to incident light is different straight lines, a with b that namely each pixel is corresponding is different.The object of flat field correction is exactly the slope and the intercept that respond straight line by changing each pixel, the output of all pixels is responded identical.But for bad point, the correction parameter of correction parameter a or b and normal pixel point has very great fluctuation process, accordingly, can realize the detection of bad point.
The assumed condition of flat field correction is sensor pixel point is linear response, and the amount of images according to adopting in correction can be divided into again peg method and multiple spot scaling method.Here adopt peg method, its step is as follows:
1) taking two width flat field image, is darkfield image, bright-field image respectively, wherein, the brightness of bright-field image preferably high-high brightness 80%, guarantee that the response curve of transducer is straight line as far as possible;
2) calculate the two point correction coefficient of each pixel based on this two width flat field image, updating formula such as formula shown in three,
Formula three:
Wherein, H and L represents bright-field image and darkfield image respectively, obtains the flat field correction coefficient of all pixels according to formula three;
3) respectively suitable threshold value T is set to correction coefficient a and b
aand T
b, in order to avoid misjudgement, it can be a scope that threshold value is chosen, such as,
the correction coefficient of more each pixel and the relation of threshold value, if correction coefficient is in threshold range, then thinks that this point is normal pixel point, otherwise be judged to be bad point.
It is to be noted; below be only a kind of concrete dead pixel detection method of the embodiment of the present invention; but should not it can be used as the restriction of scope; those skilled in the art can adopt other dead pixel detection method by correspondent transform according to actual needs, and it all should fall within protection scope of the present invention.
Step S120: the positional information according to bad point searches bad point in the image gathered, and the evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table, utilizes this interpolation algorithm to carry out interpolation processing to described bad point;
In this step successively to imageing sensor each bad point gathered in image correct, owing to having stored the positional information of bad point in step s 110, therefore, bad point timing is being carried out to image, can extracting directly bad point positional information so that search bad point (needs carry out the pixel corrected) in the picture, after finding bad point, according to the evil idea bunch type information stored in step S110, determine the evil idea bunch type corresponding to this bad point, corresponding interpolation algorithm is searched according in the mapping table that bad bunch type is preset in the step s 100, this bad point is corrected.Because the detailed process of interpolation algorithm elaborates above-mentioned, therefore, do not repeat them here.
As a kind of preferred embodiment, also determining step is comprised in step S120, be specially: before the described bunch type information of evil idea corresponding to bad point selects the interpolation algorithm matched in the mapping table, judge whether bad point is marginal point, if so, indirect assignment method is then adopted to carry out interpolation processing to described marginal point.Fig. 5 is the determination methods schematic flow sheet in step S120, is specifically described the step S120 of the present embodiment below in conjunction with Fig. 5.
As shown in Figure 5, in the present embodiment, first judge whether bad point is marginal point, if so, then adopt indirect assignment method edge point to carry out interpolation processing; Otherwise the positional information according to bad point searches bad point in the image gathered, and the evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table, utilizes this interpolation algorithm to carry out interpolation processing to described bad point.Wherein, adopt indirect assignment method edge point to carry out interpolation processing, specifically comprise: judge whether marginal point is corner point, if so, then adopt the pixel value of the horizontal adjacent pixels of marginal point point to substitute the pixel value of described marginal point; Otherwise, adopt two levels average pixel value that is adjacent or two vertical neighbor pixels of marginal point to substitute the pixel value of described marginal point.For rectangular image, marginal point refers to the pixel in rectangular image on four edges, and corner point refers to the pixel in rectangular image on four angles.
It should be pointed out that said method is mainly used in black and white image transducer, the bad point process for color image sensor needs corresponding adjustment based on the above method.The bad point process flow figure of a kind of color image sensor that Fig. 6 provides for the embodiment of the present invention, this method is applied to color image sensor, namely carry out bad point process to the coloured image that color image sensor gathers, as shown in Figure 6, it comprises the steps:
Step S200: the mapping table presetting bad bunch type and interpolation algorithm;
Step S210: according to the color of pixel in the coloured image gathered, coloured image is resolved into the submodule of different colours;
Step S220: detect the positional information of bad point in each submodule and the evil idea bunch type information corresponding to bad point successively, and store;
Due in coloured image, the benchmark of different colours component is different, therefore, in order to correctly bad point be detected, needs submodule coloured image being resolved into different colours to detect respectively.Image below in conjunction with Bayer form carries out exemplary illustration to it.
Fig. 7 is the picture structure schematic diagram of Bayer form, as shown in Figure 7, the image 1 of Bayer form is made up of the color blocks of 2 × 2, has a red pixel point (R), two green pixel points (G) and a blue pixel point (B) in each color blocks respectively.That is, in the coloured image of Bayer form, R and B component occupies 1/4, G component respectively and occupies 1/2.Before carrying out bad point detection, first Bayer picture breakdown is become the submodule of 3 kinds of colors, R and B component only accounts for 1/4 in a color blocks, can distinguish directly to extract to form red submodule 11 and blue submodule 14; G component accounts for 1/2 of color blocks, two submodules are divided into according to its position difference, first green submodule 12 is the set of the upper right corner component extracting color blocks, and second green submodule 13 is the set of the lower left corner component extracting color blocks, and final decomposition result as shown in Figure 8.Carry out bad point detection to four submodules respectively, wherein, the detection method of bad point is identical with the dead pixel detection method of black and white image transducer, does not repeat them here.
Step S230: the pixel value of different colours in coloured image is normalized, the pixel corresponding to bad point is searched according to the positional information of bad point, evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table, utilizes this interpolation algorithm to carry out interpolation processing to the pixel corresponding to described bad point;
The bad point bearing calibration of coloured image can be divided into two classes: the first kind corrects the bad point of single Color Channel; Equations of The Second Kind utilizes the correlation between each passage to carry out bad point correction.First kind bearing calibration is identical with the bad point bearing calibration of black and white image transducer, although easily realize, but it often can not obtain satisfied interpolation, and except inevitably producing some color moire fringes, also the easy edge at image produces false look.Therefore, the embodiment of the present invention adopts Equations of The Second Kind bearing calibration to carry out bad point correction to coloured image.
Equations of The Second Kind bearing calibration will utilize gradient algorithm to estimate the Grad of four distribution arrangements around bad point, but the Gradient estimates due to G component will use R, G, B tri-kinds of pixel values, the Gradient estimates of R and B component will use the pixel value of G and another color, Grad is weighed in order to unified, first need to be normalized the normal pixel value of each color, make the benchmark of R, G, B tri-kinds of colors identical; Then the Grad of four distribution arrangements is calculated.Below in conjunction with G (i, the j) point in Fig. 8, the interpolation algorithm of embodiment of the present invention coloured image is described.
First, coloured image is normalized, then, calculates the Grad of G (i, j) point in coloured image on four distribution arrangements, computing formula such as formula shown in four,
Formula four:
Secondly, the size of more above-mentioned four Grad, select the normal pixel point on the minimum direction of gradient to carry out bad point correction, updating formula is such as formula shown in five:
Formula five:
That is, in the present embodiment, first according to the image determination minimal gradient direction after normalized, then on its minimal gradient direction, use the average of similar pixel nearest with it as the pixel value waiting to revise bad point.
On the basis of said method embodiment, the present invention also provides a kind of bad point treatment system of imageing sensor, and described system comprises:
Imageing sensor, for gathering image;
Detection module, for detecting the evil idea bunch type information corresponding to the positional information of bad point and bad point;
Described evil idea bunch type is specially: the distribution pattern of peripheral bad point in the module inner peripheral pixel in image centered by bad point.For 3 × 3 modules, the definition left and right of bad point, lower-left upper right, up and down and upper left lower right be four distribution arrangements of peripheral pixel in 3 × 3 modules, occupied by peripheral bad point, the quantity of distribution arrangement and/or the quantity of peripheral bad point conclude the distribution pattern of peripheral bad point.
The distribution pattern of the peripheral bad point of described conclusion, comprising: if there is not peripheral bad point or only there is a peripheral bad point, be then summarized as the first distribution pattern; If peripheral bad point only occupies a distribution arrangement, and this distribution arrangement has two peripheral bad points, be then summarized as the second distribution pattern; If peripheral bad point occupies two distribution arrangements, be then summarized as the 3rd distribution pattern; If peripheral bad point occupies three distribution arrangements, be then summarized as the 4th distribution pattern.
First distribution pattern of described peripheral bad point, the second distribution pattern, the 3rd distribution pattern, the 4th distribution pattern bad bunch of the first kind, bad bunch of Equations of The Second Kind, bad bunch of the 3rd class, bad bunch of the 4th class respectively in corresponding bad bunch type, the mapping table of described evil idea bunch type and described interpolation algorithm, be specially: if described evil idea bunch is bad bunch of the first kind, described interpolation algorithm corresponds to: on four distribution arrangements, adopt gradient algorithm; If described evil idea bunch is bad bunch of Equations of The Second Kind, described interpolation algorithm corresponds to: on three distribution arrangements of other except distribution arrangement occupied by peripheral bad point, adopt gradient algorithm; If described evil idea bunch is the 3rd class bad bunch, described interpolation algorithm corresponds to: on two distribution arrangements of other except distribution arrangement occupied by peripheral bad point, adopt bilinear interpolation; If described evil idea bunch is the 4th class bad bunch, described interpolation algorithm corresponds to: on the distribution arrangement that there is not peripheral bad point, adopt the two-value method of average.
Memory module, for receiving and storing the evil idea bunch type information corresponding to the positional information of the bad point that detection module detects and bad point, and the mapping table of prestore bad bunch type and interpolation algorithm;
Image processing module, for receiving the image of collection, and image procossing is carried out to the image gathered, be specially: extract the positional information of bad point and the evil idea bunch type information corresponding to bad point in a storage module, positional information according to bad point searches bad point in the image gathered, evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table of memory module, utilizes this interpolation algorithm to carry out interpolation processing to described bad point.
As a kind of preferred embodiment, before the described bunch type information of evil idea corresponding to bad point selects the interpolation algorithm matched in the mapping table, also comprise: judge whether bad point is marginal point, if so, then adopt indirect assignment method to carry out interpolation processing to described marginal point; Wherein, described indirect assignment method is specially: if described marginal point is corner point, then adopt the pixel value of the horizontal adjacent pixels of described marginal point point to substitute the pixel value of described marginal point; Otherwise, adopt two of described marginal point level average pixel values that are adjacent or two vertical neighbor pixels to substitute the pixel value of described marginal point.
Above-described bad point treatment system is mainly used in black and white image transducer, and the embodiment of the present invention additionally provides a kind of bad point treatment system of color image sensor, comprising:
Color image sensor, for gathering coloured image;
Decomposing module, for resolving into the submodule of different colours by coloured image according to the color of pixel in coloured image;
Detection module, for detecting the evil idea bunch type information corresponding to the positional information of bad point in each submodule and bad point successively, and stores the information transmission detected to memory module;
Wherein, described evil idea bunch type is specially: the distribution pattern of peripheral bad point in the module inner peripheral pixel in coloured image centered by bad point.
Memory module, for receiving and storing the evil idea bunch type information corresponding to the positional information of the bad point that detection module detects and bad point, and the mapping table of prestore bad bunch type and interpolation algorithm;
Image processing module, for receiving coloured image, and image procossing is carried out to coloured image, be specially: the pixel value of different colours in coloured image is normalized, extract the positional information of bad point and the evil idea bunch type information corresponding to bad point in a storage module, positional information according to bad point searches bad point in coloured image, evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table of memory module, utilizes this interpolation algorithm to carry out interpolation processing to described bad point.
About the detailed content of each module of said system embodiment; can see the detailed description in said method embodiment; do not repeat them here; in addition; sequence of steps in said method embodiment is only an exemplary arrangement mode; those skilled in the art can correspondingly as required adjust, and should fall into equally within protection scope of the present invention.
For convenience of description, various unit is divided into describe respectively with function when describing above device.Certainly, the function of each unit can be realized in same or multiple software and/or hardware when implementing of the present invention.
Each embodiment in this specification all adopts the mode of going forward one by one to describe, between each embodiment identical similar part mutually see, what each embodiment stressed is the difference with other embodiments.Especially, for device or system embodiment, because it is substantially similar to embodiment of the method, so describe fairly simple, relevant part illustrates see the part of embodiment of the method.Apparatus and system embodiment described above is only schematic, the wherein said unit illustrated as separating component or can may not be and physically separates, parts as unit display can be or may not be physical location, namely can be positioned at a place, or also can be distributed in multiple network element.Some or all of module wherein can be selected according to the actual needs to realize the object of the present embodiment scheme.Those of ordinary skill in the art, when not paying creative work, are namely appreciated that and implement.
The above is only the specific embodiment of the present invention, those skilled in the art is understood or realizes the present invention.To be apparent to one skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein can without departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.
Claims (12)
1. a bad point processing method for imageing sensor, is characterized in that, described method comprises:
Preset the mapping table of bad bunch type and interpolation algorithm;
Detect the positional information of bad point and the evil idea bunch type information corresponding to bad point, and store;
Positional information according to bad point searches bad point in the image gathered, and the evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table, utilizes this interpolation algorithm to carry out interpolation processing to described bad point;
Wherein, described evil idea bunch type is specially: the distribution pattern of peripheral bad point in the module inner peripheral pixel in image centered by bad point.
2. the bad point processing method of imageing sensor according to claim 1, it is characterized in that, described module centered by bad point is 3 × 3 modules, the definition left and right of bad point, lower-left upper right, up and down and upper left lower right be four distribution arrangements of peripheral pixel in 3 × 3 modules, occupied by peripheral bad point, the quantity of distribution arrangement and/or the quantity of peripheral bad point conclude the distribution pattern of peripheral bad point.
3. the bad point processing method of imageing sensor according to claim 2, is characterized in that, the distribution pattern of the peripheral bad point of described conclusion, comprising:
If there is not peripheral bad point or only there is a peripheral bad point, be then summarized as the first distribution pattern;
If peripheral bad point only occupies a distribution arrangement, and this distribution arrangement has two peripheral bad points, be then summarized as the second distribution pattern;
If peripheral bad point occupies two distribution arrangements, be then summarized as the 3rd distribution pattern;
If peripheral bad point occupies three distribution arrangements, be then summarized as the 4th distribution pattern.
4. the bad point processing method of imageing sensor according to claim 3, it is characterized in that, first distribution pattern of described peripheral bad point, the second distribution pattern, the 3rd distribution pattern, the 4th distribution pattern bad bunch of the first kind, bad bunch of Equations of The Second Kind, bad bunch of the 3rd class, bad bunch of the 4th class respectively in corresponding bad bunch type, the mapping table of described evil idea bunch type and described interpolation algorithm, is specially:
If described evil idea bunch is bad bunch of the first kind, described interpolation algorithm corresponds to: on four distribution arrangements, adopt gradient algorithm;
If described evil idea bunch is bad bunch of Equations of The Second Kind, described interpolation algorithm corresponds to: on three distribution arrangements of other except distribution arrangement occupied by peripheral bad point, adopt gradient algorithm;
If described evil idea bunch is the 3rd class bad bunch, described interpolation algorithm corresponds to: on two distribution arrangements of other except distribution arrangement occupied by peripheral bad point, adopt bilinear interpolation;
If described evil idea bunch is the 4th class bad bunch, described interpolation algorithm corresponds to: on the distribution arrangement that there is not peripheral bad point, adopt the two-value method of average.
5. the bad point processing method of imageing sensor according to claim 1, is characterized in that, before the described bunch type information of evil idea corresponding to bad point selects the interpolation algorithm matched in the mapping table, also comprises:
Judge whether bad point is marginal point, if so, then adopt indirect assignment method to carry out interpolation processing to described marginal point;
Wherein, described indirect assignment method is specially: if described marginal point is corner point, then adopt the pixel value of the horizontal adjacent pixels of described marginal point point to substitute the pixel value of described marginal point; Otherwise, adopt two of described marginal point level average pixel values that are adjacent or two vertical neighbor pixels to substitute the pixel value of described marginal point.
6. a bad point processing method for imageing sensor, for color image sensor, is characterized in that, described method comprises:
Preset the mapping table of bad bunch type and interpolation algorithm;
According to the color of pixel in the coloured image gathered, coloured image is resolved into the submodule of different colours;
Detect the positional information of bad point in each submodule and the evil idea bunch type information corresponding to bad point successively, and store;
The pixel value of different colours in coloured image is normalized, the pixel corresponding to bad point is searched according to the positional information of bad point, evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table, utilizes this interpolation algorithm to carry out interpolation processing to the pixel corresponding to described bad point;
Wherein, described evil idea bunch type is specially: the distribution pattern of peripheral bad point in the module inner peripheral pixel in coloured image centered by bad point.
7. a bad point treatment system for imageing sensor, is characterized in that, described system comprises:
Imageing sensor, for gathering image;
Detection module, for detecting the evil idea bunch type information corresponding to the positional information of bad point and bad point;
Memory module, for receiving and storing the evil idea bunch type information corresponding to the positional information of the bad point that detection module detects and bad point, and the mapping table of prestore bad bunch type and interpolation algorithm;
Image processing module, for receiving the image of collection, and image procossing is carried out to the image gathered, be specially: extract the positional information of bad point and the evil idea bunch type information corresponding to bad point in a storage module, positional information according to bad point searches bad point in the image gathered, evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table of memory module, utilizes this interpolation algorithm to carry out interpolation processing to described bad point;
Wherein, described evil idea bunch type is specially: the distribution pattern of peripheral bad point in the module inner peripheral pixel in image centered by bad point.
8. the bad point treatment system of imageing sensor according to claim 7, it is characterized in that, described module centered by bad point is 3 × 3 modules, the definition left and right of bad point, lower-left upper right, up and down and upper left lower right be four distribution arrangements of peripheral pixel in 3 × 3 modules, occupied by peripheral bad point, the quantity of distribution arrangement and/or the quantity of peripheral bad point conclude the distribution pattern of peripheral bad point.
9. the bad point treatment system of imageing sensor according to claim 8, is characterized in that, the distribution pattern of the peripheral bad point of described conclusion, comprising:
If there is not peripheral bad point or only there is a peripheral bad point, be then summarized as the first distribution pattern;
If peripheral bad point only occupies a distribution arrangement, and this distribution arrangement has two peripheral bad points, be then summarized as the second distribution pattern;
If peripheral bad point occupies two distribution arrangements, be then summarized as the 3rd distribution pattern;
If peripheral bad point occupies three distribution arrangements, be then summarized as the 4th distribution pattern.
10. the bad point treatment system of imageing sensor according to claim 9, it is characterized in that, first distribution pattern of described peripheral bad point, the second distribution pattern, the 3rd distribution pattern, the 4th distribution pattern bad bunch of the first kind, bad bunch of Equations of The Second Kind, bad bunch of the 3rd class, bad bunch of the 4th class respectively in corresponding bad bunch type, the mapping table of described evil idea bunch type and described interpolation algorithm, is specially:
If described evil idea bunch is bad bunch of the first kind, described interpolation algorithm corresponds to: on four distribution arrangements, adopt gradient algorithm;
If described evil idea bunch is bad bunch of Equations of The Second Kind, described interpolation algorithm corresponds to: on three distribution arrangements of other except distribution arrangement occupied by peripheral bad point, adopt gradient algorithm;
If described evil idea bunch is the 3rd class bad bunch, described interpolation algorithm corresponds to: on two distribution arrangements of other except distribution arrangement occupied by peripheral bad point, adopt bilinear interpolation;
If described evil idea bunch is the 4th class bad bunch, described interpolation algorithm corresponds to: on the distribution arrangement that there is not peripheral bad point, adopt the two-value method of average.
The bad point treatment system of 11. imageing sensors according to claim 7, is characterized in that, before the described bunch type information of evil idea corresponding to bad point selects the interpolation algorithm matched in the mapping table, also comprises:
Judge whether bad point is marginal point, if so, then adopt indirect assignment method to carry out interpolation processing to described marginal point;
Wherein, described indirect assignment method is specially: if described marginal point is corner point, then adopt the pixel value of the horizontal adjacent pixels of described marginal point point to substitute the pixel value of described marginal point; Otherwise, adopt two of described marginal point level average pixel values that are adjacent or two vertical neighbor pixels to substitute the pixel value of described marginal point.
The bad point treatment system of 12. 1 kinds of imageing sensors, is characterized in that, described system comprises:
Color image sensor, for gathering coloured image;
Decomposing module, for resolving into the submodule of different colours by coloured image according to the color of pixel in coloured image;
Detection module, for detecting the evil idea bunch type information corresponding to the positional information of bad point in each submodule and bad point successively, and stores the information transmission detected to memory module;
Memory module, for receiving and storing the evil idea bunch type information corresponding to the positional information of the bad point that detection module detects and bad point, and the mapping table of prestore bad bunch type and interpolation algorithm;
Image processing module, for receiving coloured image, and image procossing is carried out to coloured image, be specially: the pixel value of different colours in coloured image is normalized, extract the positional information of bad point and the evil idea bunch type information corresponding to bad point in a storage module, positional information according to bad point searches bad point in coloured image, evil idea bunch type information corresponding to bad point selects the interpolation algorithm matched in the mapping table of memory module, utilizes this interpolation algorithm to carry out interpolation processing to described bad point;
Wherein, described evil idea bunch type is specially: the distribution pattern of peripheral bad point in the module inner peripheral pixel in coloured image centered by bad point.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106385549A (en) * | 2016-09-05 | 2017-02-08 | 凌云光技术集团有限责任公司 | Image bad point correction method and related device thereof |
| CN106878635A (en) * | 2016-12-14 | 2017-06-20 | 中国资源卫星应用中心 | A kind of compensation method of invalid pixel |
| CN107305695A (en) * | 2016-04-14 | 2017-10-31 | 上海富瀚微电子股份有限公司 | A kind of automatic bad point means for correcting of image and method |
| CN107613229A (en) * | 2017-09-30 | 2018-01-19 | 杭州雄迈集成电路技术有限公司 | A kind of dead pixels of image sensor surveys means for correcting and method |
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| CN112596965A (en) * | 2020-12-14 | 2021-04-02 | 上海集成电路研发中心有限公司 | Digital image bad cluster statistical method and automatic integrated circuit tester |
| CN112911174A (en) * | 2021-01-18 | 2021-06-04 | 珠海全志科技股份有限公司 | Image dead pixel cluster correction method, computer device and computer readable storage medium |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050280805A1 (en) * | 2004-05-28 | 2005-12-22 | Hoya Corporation | Mura defect inspection mask, apparatus and method of inspecting the mura defect, and method of producing a photomask |
| CN101304485A (en) * | 2008-05-26 | 2008-11-12 | 北京中星微电子有限公司 | Method and apparatus for compensating bad point |
| CN101588447A (en) * | 2008-05-23 | 2009-11-25 | 比亚迪股份有限公司 | Method and system for correcting dead pixel points of images and image sensor |
| CN104700424A (en) * | 2015-03-30 | 2015-06-10 | 山东省计量科学研究院 | Medical colorful electronic endoscopy image bad point detection device |
| CN104754327A (en) * | 2015-03-26 | 2015-07-01 | 山东大学 | Method for detecting and eliminating defective pixels of high spectral image |
| CN104835125A (en) * | 2015-05-08 | 2015-08-12 | 上海联影医疗科技有限公司 | Defect pixel correction method for flat-panel detector |
-
2015
- 2015-10-20 CN CN201510684449.9A patent/CN105306843B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050280805A1 (en) * | 2004-05-28 | 2005-12-22 | Hoya Corporation | Mura defect inspection mask, apparatus and method of inspecting the mura defect, and method of producing a photomask |
| CN101588447A (en) * | 2008-05-23 | 2009-11-25 | 比亚迪股份有限公司 | Method and system for correcting dead pixel points of images and image sensor |
| CN101304485A (en) * | 2008-05-26 | 2008-11-12 | 北京中星微电子有限公司 | Method and apparatus for compensating bad point |
| CN104754327A (en) * | 2015-03-26 | 2015-07-01 | 山东大学 | Method for detecting and eliminating defective pixels of high spectral image |
| CN104700424A (en) * | 2015-03-30 | 2015-06-10 | 山东省计量科学研究院 | Medical colorful electronic endoscopy image bad point detection device |
| CN104835125A (en) * | 2015-05-08 | 2015-08-12 | 上海联影医疗科技有限公司 | Defect pixel correction method for flat-panel detector |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107305695A (en) * | 2016-04-14 | 2017-10-31 | 上海富瀚微电子股份有限公司 | A kind of automatic bad point means for correcting of image and method |
| CN106385549A (en) * | 2016-09-05 | 2017-02-08 | 凌云光技术集团有限责任公司 | Image bad point correction method and related device thereof |
| CN106385549B (en) * | 2016-09-05 | 2019-04-26 | 凌云光技术集团有限责任公司 | Dead pixel points of images antidote and relevant apparatus |
| CN106878635B (en) * | 2016-12-14 | 2019-06-11 | 中国资源卫星应用中心 | A kind of compensation method of invalid pixel |
| CN106878635A (en) * | 2016-12-14 | 2017-06-20 | 中国资源卫星应用中心 | A kind of compensation method of invalid pixel |
| CN108701218A (en) * | 2017-02-03 | 2018-10-23 | 华为技术有限公司 | A kind of method, apparatus and terminal of fingerprint collecting |
| CN108701218B (en) * | 2017-02-03 | 2021-02-12 | 华为技术有限公司 | Fingerprint acquisition method and device and terminal |
| CN107613229A (en) * | 2017-09-30 | 2018-01-19 | 杭州雄迈集成电路技术有限公司 | A kind of dead pixels of image sensor surveys means for correcting and method |
| CN110807731A (en) * | 2018-07-20 | 2020-02-18 | 宁波舜宇光电信息有限公司 | Method, apparatus, system and storage medium for compensating image dead pixel |
| CN109348146A (en) * | 2018-10-31 | 2019-02-15 | 上海集成电路研发中心有限公司 | A kind of static bad point means for correcting of imaging sensor and its bearing calibration |
| CN109348146B (en) * | 2018-10-31 | 2021-03-02 | 上海集成电路研发中心有限公司 | Static dead pixel correction device and method for image sensor |
| CN109348216A (en) * | 2018-11-28 | 2019-02-15 | 凌云光技术集团有限责任公司 | A kind of combination treatment method of bad point detection peace field calibration |
| CN109685794A (en) * | 2018-12-25 | 2019-04-26 | 凌云光技术集团有限责任公司 | For mobile phone screen defects detection camera adaptive step DPC algorithm and device |
| CN109685794B (en) * | 2018-12-25 | 2021-01-29 | 凌云光技术股份有限公司 | Camera self-adaptive step length DPC algorithm and device for mobile phone screen defect detection |
| CN111681193A (en) * | 2020-06-12 | 2020-09-18 | 电子科技大学 | Optimization of FPGA-based infrared image dead pixel correction system |
| CN112596965A (en) * | 2020-12-14 | 2021-04-02 | 上海集成电路研发中心有限公司 | Digital image bad cluster statistical method and automatic integrated circuit tester |
| CN112596965B (en) * | 2020-12-14 | 2024-04-09 | 上海集成电路研发中心有限公司 | Digital image bad cluster statistical method and integrated circuit automatic tester |
| CN112911174A (en) * | 2021-01-18 | 2021-06-04 | 珠海全志科技股份有限公司 | Image dead pixel cluster correction method, computer device and computer readable storage medium |
| CN113873229A (en) * | 2021-09-26 | 2021-12-31 | 江西盛泰精密光学有限公司 | Image dead pixel detection method, system and device |
| CN113873229B (en) * | 2021-09-26 | 2024-02-27 | 江西盛泰精密光学有限公司 | Image dead pixel detection method, system and device |
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